Home › Categories › Technology› NUIG-UNLP At SemEval-2016 Task 1: Soft Alignment And Deep Learning For Semantic Textual Similarity

NUIG-UNLP At SemEval-2016 Task 1: Soft Alignment And Deep Learning For Semantic Textual Similarity.pdf

S16-1110.pdf

Preview of NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity

🔗 Source: aclanthology.org

📊 Size: 475 KB

👤 Author: John Philip McCrae ; Kartik Asooja ; Nitish Aggarwal ; Paul Buitelaar

⬇️ Downloads: 52

Summary

They introduce soft alignment, a method that produces a score indicating the likelihood of words in one sentence aligning with words in another, instead of creating hard word correspondences.

Key Contributions:

Multi-Feature System: Combines various features based on simple metrics (like LCS, n-gram overlap) and novel deep learning techniques for robust semantic similarity calculation.
Soft Alignment: Leverages pre-trained neural word embeddings (WordSim) to create soft alignment matrices reflecting the semantic relatedness between words across sentences.
* Deep Learning Model: Uses a BiLSTM network to capture word context within sentences, complementing the soft alignment information.

Evaluation:

The system was evaluated on five datasets at SemEval 2016 STS Task 1, achieving above-median scores in four of them. The authors also report Pearson's Correlation for various configurations during development (see Table 1), demonstrating the effectiveness of their multi-feature approach.

Comparison:

They compare their system to Sultan et al.'s method (with and without Jacana aligner) and demonstrate significant improvements, especially when incorporating soft alignment and deep learning components.

Overall:

The paper presents a comprehensive exploration of different techniques for semantic textual similarity, highlighting the benefits of combining simple metrics with novel deep learning approaches like soft alignment and BiLSTM networks.

Download File

Description

The paper introduces a multi-feature system for semantic text similarity, utilizing soft alignment and Explicit Semantic Analysis techniques, achieving top performance in 4 out of 5 datasets at SemEval-2016 Task 1.

Technical Information

File Format: PDF
File Size: 475 KB
Pages: 6
Language: EN
Author: John Philip McCrae ; Kartik Asooja ; Nitish Aggarwal ; Paul Buitelaar
Total Downloads: 52
Last Updated: 3 hours ago

Document Overview

This PDF document about NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity.

Related Topics

If you're interested in NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity, you might also want to explore:

Download NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity eBooks for free and learn more about NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity. These books contain exercises and tutorials to improve your practical skills, at all levels!

Not satisfied with this document? We have related documents to NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity, try searching with similar keywords: NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity, Nuig Unlp At Semeval 2016 Task 1 Soft Alignment And Deep Learning For Semantic Textual Similarity, A Semantic Search Engine Using Semantic Similarity, SemEval-2023 Task 4, Självkänsla och KBT ‐ Påverkas självkänslan vid KBT för depression och ångesttillstånd?Se lf‐esteem and CBT ‐ How does CBT for de, "Hinder och möjliggörare för 1.5°-livsstilar: Ytliga och djupgående strukturella faktorer som påverkar potentialen för hållbar k, Ändring av genomföranderam för en europeisk plattform för utbyte av balansenergi från frekvensåterställn ingsreserver med manuell, Rekommendationer för vaccination mot covid-19 för särskilda grupper av barn -

You can download PDF versions of the user's guide, manuals and ebooks about NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity, you can also find and download for free A free online manual (notices) with beginner and intermediate, Downloads Documentation, You can download PDF files (or DOC and PPT) about NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity for free, but please respect copyrighted ebooks.

NUIG-UNLP At SemEval-2016 Task 1: Soft Alignment And Deep Learning For Semantic Textual Similarity.pdf

Summary

Description

Technical Information

Document Overview

Related Topics

Related Categories

Related Documents

Seznam podporovaných vozidel (214.96 KB)

UUCEF Current & Historical Fees

PDF

Property Alignment on the Semantic Web

Deep Learning